scholarly journals Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Zhiyuan Ma ◽  
Mao Zong ◽  
Xiangrong Zhou ◽  
...  
Keyword(s):  
Ct Images ◽  
Experimental Results ◽  
Initial Contour ◽  
Segmentation Result ◽  
Ct Image ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Running Time ◽  
Novel Method ◽  
Liver Region

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

CT Images Segmentation Based on Dynamic Relative Fuzzy Region Growing Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4233-4236
Author(s):  
Zhen You Zhang ◽  
Guo Huan Lou
Keyword(s):  
Confidence Interval ◽  
Region Growing ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Segmentation Result ◽  
Ct Image ◽  
Initial Segmentation ◽  
Fuzzy Region

Segmentation algorithm of CT Image is discussed in this paper. Dynamic relative fuzzy region growing algorithm is used for CT. At the beginning of the segmentation, the confidence interval region growing algorithm is used. The overlapping parts in the initial segmentation result is segmented again with the improved fuzzy connected, and then determine which region the overlapping parts belong to. Thus, the final segmentation result can be obtained. Since the algorithm contains the advantages of region growing algorithm, fuzzy connected algorithm and the region competition, the runtime of segmentation is greatly reduced and better experimental results are obtained.

Semi-Automatic Liver Segmentation Using Improved GVF Snake Model

2010 ◽  
Vol 121-122 ◽  
pp. 435-440 ◽  
Author(s):  
Hui Yan Jiang ◽  
Xi He Gao
Keyword(s):  
Force Field ◽  
External Force ◽  
Left Lobe ◽  
Initial Contour ◽  
Ct Image ◽  
Liver Segmentation ◽  
Threshold Method ◽  
Abdominal Ct ◽  
Snake Model ◽  
External Force Field

Snakes are extensively used in computer vision and Image processing. However, when it comes to the liver segmentation from computed tomography (CT) image, the application of the models is limited because it can not extend to certain boundary indentations of the liver. In order to solve this problem, we developed an improved GVF snake model by adding an external force field which can efficiently attract the initial contour to these depression areas, such as the top of the left lobe of liver. The proposed method includes two steps. Firstly, combined with the threshold method and the morphology operation, our model can acquire the initial contour of the liver. Secondly, we create an imposed external force field through the interaction with the system, and we make the initial contour converge under the influence of both GVF field and imposed external force field to get the accurate contour of the liver. The application of this method on abdominal CT image is demonstrated, both qualitatively and quantitatively.

scholarly journals A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Hiroshi Fujita
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Negative Impact ◽  
Level Set Methods ◽  
Region Growing ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Ct Image ◽  
Fast Marching

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

scholarly journals U-Net combined with multi-scale attention mechanism for liver segmentation in CT images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiawei Wu ◽  
Shengqiang Zhou ◽  
Songlin Zuo ◽  
Yiyin Chen ◽  
Weiqin Sun ◽  
...  
Keyword(s):  
Work Experience ◽  
Spatial Location ◽  
Ct Images ◽  
Attention Mechanism ◽  
Dice Similarity Coefficient ◽  
Liver Segmentation ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Spatial Location Information ◽  
Liver Region

Abstract Background The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).

scholarly journals Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Weiwei Wu ◽  
Zhuhuang Zhou ◽  
Shuicai Wu ◽  
Yanhua Zhang
Keyword(s):  
Liver Volume ◽  
Maximum Intensity Projection ◽  
Liver Slice ◽  
Graph Cuts ◽  
Ct Images ◽  
Patient Specific ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Liver Segmentation ◽  
Abdominal Ct

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

scholarly journals Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuhan Yang ◽  
Shoujun Zhou ◽  
Peng Shang ◽  
En Qi ◽  
Shibin Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Active Contour ◽  
Ct Images ◽  
Deformable Registration ◽  
Initial Contour ◽  
Ct Image ◽  
Speeded Up Robust Features ◽  
Contour Propagation ◽  
Feature Based ◽  
Automatic Contour

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

A New Contour Initialization of CT Image Sequences in GVF Model

2013 ◽  
Vol 321-324 ◽  
pp. 1225-1229
Author(s):  
Jie Zhao ◽  
Fa Ling Yi ◽  
Zhan Peng Huang
Keyword(s):  
Cancer Detection ◽  
Image Sequences ◽  
Initial Contour ◽  
Watershed Algorithm ◽  
Segmentation Result ◽  
Ct Image ◽  
Gray Scale ◽  
Snake Model ◽  
Serial Images ◽  
Initialization Algorithm

In order to overcome the shortcomings that GVF model is susceptible to structures with slender topology, an improved watershed algorithm is proposed to determine initial contour of GVF Snake model. The organs in medical CT images often were irregular and had deep boundary concavities, and CT serial images were up to hundreds pieces. Firstly, in a CT image the improved watershed algorithm grows an organ from a seed block by the principle of the similarity between gray scale and texture, then using its edge as the start contour of the adjacent CT sequence, with GVF algorithm segment the organ from a sequence of images, the process is repeated until get all slices of the entire abdomen. Experiment results which are the basic of 3-D reconstruction and cancer detection show that the new contour initialization algorithm can obtain segmentation result efficiently, accurately and cost less time.

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